Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performan...
The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the...
Over the past decade, significant technological advancements have led to a substantial increase in data proliferation. Both scientific computation and Big Data workloads play a central role, manipulating massive data and challenging conventional high...
The widening performance gap between processor speed and memory access latency has made data locality a critical bottleneck in high-performance computing. In Non-Uniform Memory Access (NUMA) and distributed memory systems, remote accesses incur penal...
Network bandwidth is a scarce resource in big data environments, so data locality is a fundamental problem for data-parallel frameworks such as Hadoop and Spark. This problem is exacerbated in multicore server-based clusters, where multiple tasks run...
Big data has revolutionized science and technology leading to the transformation of our societies. High-performance computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditiona...
Locality-sensitive hashing (LSH) has been widely used for c-approximate nearest neighbor search (c-ANNS) in high-dimensional spaces. However, its retrieval performance degrades as the dataset size grows. To address this limitation, we propose a data-...
Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and posse...
The use of wireless signals for the purposes of localization enables a host of applications relating to the determination and verification of the positions of network participants ranging from radar to satellite navigation. Consequently, this has bee...
Vehicle localization using mounted sensors is an essential technology for various applications, including autonomous vehicles and road mapping. Achieving high positioning accuracy through the fusion of low-cost sensors is a topic of considerable inte...
Key performance indicators (KPIs) are time series with the format of (timestamp, value). The accuracy of KPIs anomaly detection is far beyond our initial expectations sometimes. The reasons include the unbalanced distribution between the normal data...
This systematic review examines the usefulness of geospatial data for localization and prioritization of sustainable development goals (SDGs) and where they are applicable to the local level. This study reviewed 147 peer-reviewed articles and identif...
Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is...
The proposal of local differential privacy solves the problem that the data collector must be trusted in centralized differential privacy models. The statistical analysis of numerical data under local differential privacy has been widely studied by m...
Multiple object tracking is used in a range of applications, including autonomous control of various vehicles and sports analytics. The main challenges in developing tracking systems are detection errors and computational load that scales with the nu...
The fields of indoor localization and positioning have seen extensive research in recent years. Their scientific soundness is of great importance, as information about an entity’s location in indoor environments can lead to innovative services...
Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various priv...
The paper presents the results of an experimental investigation and statistical analysis of the data streams generated by popular Internet services. It is found that every investigated Internet service generates data streams of a specific type, posse...
The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which ha...
In this paper, a new family of binary LRCs (BLRCs) with locality 2 and uneven availabilities for hot data is proposed, which has a high information symbol availability and low parity symbol availabilities for the local repair of distributed storage s...
The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization...
In this paper, we consider the problem of accurate, transparent, and interpretable detection, as well as the localization of false data injection attacks (FDIAs) in smart grids. In order to address that problem, we employ our knowledge discovery mach...
Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Fur...
Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and t...
In ensemble data assimilation systems, the impracticalities of full sampling and systematic error often lead to spurious correlations between two variables with low actual correlations. To solve these problems, researchers have previously proposed a...
Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayes...
In this paper, a local-moment-driven robust reversible data hiding (LM-RRDH) scheme is proposed, which can provide security to hidden messages against unintentional modifications. The proposed LM-RRDH decomposes an image into LSB and MSB planes and t...
Indoor positioning of mobile devices plays a key role in many aspects of our daily life. These include real-time people tracking and monitoring, activity recognition, emergency detection, navigation, and numerous location based services. Despite many...
Large spontaneous leakages in district heating networks (DHNs) require a separation of the affected network part, as interruption of the heat supply is imminent. Measurement data of 22 real events was analyzed for localization, but suitable results w...
There is a growing dependence of critical and safety-of-life systems on the Global Navigation Satellite System (GNSS). GNSS interference events can cause severe impacts on aircraft safety, including unavailability of GNSS-based landing services. Ther...
Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML)...
Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will serious...
In recent years, smart grids have integrated information and communication technologies into power networks, which brings new network security issues. Among the existing cyberattacks, the false data injection attack (FDIA) compromises state estimatio...
Modern HFC (Hybrid Fiber–Coaxial) networks comprise millions of users. It is of great importance for HFC network operators to provide high network access availability to their users. This requirement is becoming even more important given the in...
The point cloud is one of the measurement results of local measurement and is widely used because of its high measurement accuracy, high data density, and low environmental impact. However, since point cloud data from a single measurement are general...
In the context of automatic charging for electric vehicles, collision localization for the end-effector of robots not only serves as a crucial visual complement but also provides essential foundations for subsequent response design. In this scenario,...
Background: Precise localization of acupuncture points (acupoints) is crucial for the clinical success of Traditional Korean Medicine (TKM). Traditional methods that rely on visual inspection and palpation are subjective and prone to inter- and intra...
Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlie...
As big data and data mining technology advance, research on the collection and analysis of medical data on the internet of medical things (IoMT) has gained increasing attention. Medical institutions often collect users’ signs and symptoms from...
This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementat...
Thanks to the increasing popularity of location-based social networks, a large amount of user-generated geo-referenced check-in data is now available, and such check-in data is becoming a new data source in the study of mobility and travel. Conventio...
As the usage of solar power generation increases, it has become essential to predict power generation accurately. Among the various factors that affect solar power generation, soiling on the panel surface drastically reduces solar power generation. T...
We propose a novel method using Locality-Sensitive Hashing (LSH) for solving the optimization problem that arises in the training stage of support vector machines for large data sets, possibly in high dimensions. LSH was introduced as an efficient wa...
In recent years, with the rapid development of intelligent technology, information security and privacy issues have become increasingly prominent. Epidemiological survey data (ESD) research plays a vital role in understanding the laws and trends of d...
This paper presents a comprehensive framework for the reliability analysis of ship local structures. Existing reliability analysis of ship local structures relies on empirical analysis without experimental validation. The presented framework improves...
Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses...
Longitudinal data modeling is widely carried out using parametric methods. However, when the parametric model is misspecified, the obtained estimator might be severely biased and lead to erroneous conclusions. In this study, we propose a new estimati...
This paper presents a new data-driven method for leak localization in water distribution networks. The proposed method relies on the use of available pressure measurements in some selected internal network nodes and on the estimation of the pressure...
The inherent energy constraints of sensor nodes render energy efficiency optimization a critical challenge in wireless sensor network deployments. This study presents an innovative acoustic source localization framework incorporating a two-level data...
The limited availability of high-resolution monitoring systems for the drought phenomena and water dynamics affected by weather anomalies hinders policy decisions in a multitude of ways. This paper introduces the availability of the high-resolution W...
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